The engine room of @Google. Building AI safely and responsibly to solve the world’s most complex problems. Join us: https://xitter.twitterapi.xyz/t.co/jUHQA27iBL
London, UK
Joined January 2016
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Gemini Robotics 2.5 is here. 🤖
Our latest physical AI is able to learn hundreds of everyday skills - from folding laundry and serving pastries to autonomously sorting car parts on the factory floor.
Here’s a snapshot of its capabilities 🧵
Finding the exact genetic cause of a rare disease is like looking for a needle in a haystack. 🪡
On our podcast, @fryrsquared explores with @Avsecz and @adamgayoso how AlphaGenome 2 helps scientists test millions of DNA changes to find vital clues for human health.
Timecodes:
00:00 Introduction to AlphaGenome
04:01 Genotype, Phenotype & DNA
08:30 Transformers to AlphaGenome
12:11 Mapping the Genome
17:45 Rare disease cases
21:10 AlphaGenome 2
30:06 Future vision
38:45 Hannah's thoughts
Watch → goo.gle/3Vgj7LX
Spotify → goo.gle/4zRA8Lt
Apple Podcasts → goo.gle/4BZEaTx
Or listen wherever you get your podcasts! 🎧
Google DeepMind retweeted
Our Gemini team @GoogleDeepMind is making tremendous progress.
Gemini 4 Carbon’s coding and scientific reasoning capabilities are a step change and have accelerated our own science workflows.
Carbon has already helped our team:
- Debug complex experimental flows. In one complex case, it traced anomalous assay results back to a faulty liquid handler and reagent carryover between high-throughput experimental runs.
- Develop strategies to mine massive datasets, making models like AlphaFold 3 even more effective.
- Compress computational workflows like cheminformatics tasks from weeks down to hours.
I am happy to share that Gemini 4 Carbon is now generally available. Together with our updated Antigravity Science plugin, Carbon can greatly assist the work of scientists.
I am really looking forward to seeing scientists across the world use it for their work.
Your heart, brain, and immune system share the same DNA sequence. So how can a single-letter change affect disease in some cells, while sparing the rest?
AlphaGenome 2 is our new AI model that zooms in on specific cells to help scientists understand what drives disease. 🧵
The model also adapts to lab experiments on the fly with no retraining needed.
Researchers can plug in data from just a few dozen cells to see how DNA changes behave in specific moments, like:
🔘 Immune cells responding to a vaccine
🔘 Specific cells in the gut driving inflammatory disease
🔘 Stem cells maturing into blood cells
By predicting how the same DNA is read differently from cell to cell, AlphaGenome 2 opens up entirely new possibilities for understanding human genetics.
It’s available now for non-commercial research via our API and interactive Colab tutorials. Find out more → goo.gle/4zikg3w
Google DeepMind retweeted
Announcing a major milestone achieved by my team @GoogleDeepMind: we have proven key security properties of the machine code underlying the Signal messaging protocol. This is the first time the actual production code of a widely deployed protocol has been formally verified – by any method, with or without AI – and at this depth.
More than a research breakthrough, it’s a prime example of how frontier AI can be a force multiplier to counter the speed and scale of modern cyber threats.
Formal verification offers a higher standard of security; however, it’s extremely complex and time consuming, which is why in many cases it’s considered to be impractical. Similar efforts could take teams decades of combined effort. We did it in a month’s time.
Using AlphaProof Code, Google DeepMind's new agentic framework for formal verification that runs on LLMs including Gemini Carbon, we verified the Signal protocol down to the actual code running on devices. This milestone lays the groundwork for a future in which vital systems, from medical device firmware to power grid software, ship with mathematical proof that their protections work.
Reaching that future will take continued research from our community. In that spirit, we're open-sourcing our formal specifications and verification artifacts so others can build on this work.
On a personal note, this moment is of deep significance to me. I started my own research journey more than two decades ago as an intern working on checking the consistency of complex software systems. Seeing this grand challenge made practical with modern AI is extraordinary.
I'm extremely proud of my team members: Swarat Chaudhuri, Andrew Ferraiuolo, Anton Kovsharov, Tej Chajed, Sam Schlesinger, Karn Seth, Jonathan Katz, and Charles Sutton for taking on this grand challenge and delivering, and grateful to the Signal team for their partnership.
deepmind.google/blog/formal-…
For the first time, AI has mathematically proven @signalapp’s protocol is secure down to the code level.
Using our breakthrough AlphaProof Code framework, we verified the messaging app’s cryptographic system – which protects billions of private messages every day.
Gemini agents generated over 6M lines of proofs in Lean, auditing the code so human experts only had to review the critical 1%.
This is a key step toward using AI to verify vital infrastructure we all rely on – from daily communication to power grids. Find out more → goo.gle/4BLrkbk
Start building with Gemini 4 Carbon: our strongest model for complex, long-horizon tasks.
It’s designed to explore problems thoroughly and run extended verification loops across software engineering, cybersecurity, knowledge work, and science.
From synthesizing dense multimodal files to building interactive 3D web experiences, or even uncovering software vulnerabilities, Carbon helps solve problems that require sustained reasoning.
Now rolling out in:
🔘 The @GeminiApp and AI Mode in Search for Google AI Ultra subscribers.
🔘 Gemini 4 Carbon is available via the API in @GoogleAIStudio and Gemini for business for all paying developers and enterprises
🔘 Gemini for business, CodeMender, and Google Antigravity for enterprise use
goo.gle/48Jd6ud
With @ArtificialAnlys, we built MKA (Multimodal Knowledge Agent): a benchmark for how well agents handle visually complex tasks.
Everyday professional work often uses more than just text – from cross-referencing blueprints to reading scientific charts. This is not covered by existing evaluations.
Gemini 4 Carbon ranks #2, at 6x lower average cost per task than the top model. → goo.gle/4AikIja
In a proof of concept with @Apptronik and @MercedesBenz Gemini Robotic 2.5 helped the Apollo 2 robot pick and organize car parts for assembly with 96% accuracy.
As robots move into more real-world spaces, they need greater safety measures. We’ve built in new ways for them to spot hazards, steer away from humans, and test against jailbreaks.
FInd out more about Gemini Robotics 2.5 → goo.gle/4zBBUA6
We’re sharing new research demonstrating how AI agents can accelerate the science of stress-testing encryption.
Guided by humans, our models uncovered two major weaknesses in legacy 1024-bit RSA cryptosystems.
Our VP Science @Pushmeet explains more ↓ xitter.twitterapi.xyz/pushmeet/status/211875…
